Light Amplification of Images
If one were to photograph the stain glass windows of a Cathedral, the resulting image would be typically too dark for modern tastes. The beauty of the actual stained glass is lost. Further, a photographer can increase depth of field of an image by reducing the device's aperture, but light collection is sacrificed and can also result in a dark image. Further, as a still image device is capable of only HSV color space, Kuo then isolates and removes the color information (Hue) from the remaining image components (saturation and intensity). Kuo suggests that the components of saturation and intensity can be enhanced without introducing distortion into the color or Hue component. Kuo's color image is represented by a plurality of pixels in HSV color space. Once transformed, Kuo inverse transforms HSV back to RGB color space, all the while claiming this to be efficient. In the preferred embodiment, Kuo adjusts intensity (V) and saturation (S). In summary, Kuo first transforms RGB to HSV color space, applies two sequential transformation functions to V and S respectively, and finally inverse transforms the altered HSV back to RGB for display.
Respectfully, Applicant asserts that manipulation of the saturation does affect the color and thus the Kuo technique does not result in true color enhancement. Color photos have three degrees of freedom, being R,G and B, where as black and white photos only have one. Any transformation of the three values results in three more values, each of which contains a color component and not merely a single color value (i.e. hue) and two other independent structural components (i.e. saturation and intensity). Hue may be a more extreme sense of color than saturation, but saturation is still a color component. Adjusting a dot's saturation results in a change in the proportions of RGB in the dot and hence its color. If the saturation is changed, say as part of brightening the image, the result is not the same as if the recording device or camera had obtained the image directly from the real world subject under brighter conditions, or more exposure.
Further, note that Kuo emphasizes and attempts to minimize the computational overhead or expense. Unfortunately, Kuo introduces two RGB-HSV and HSV-RGB transformations in addition to whatever adjustments (preferably two) Kuo makes to the HSV pixel. A transformation from RGB to HSV color space, and back again, involves the use of computation-intensive mathematical functions.
Further, the foregoing methodologies rely on significant user input and skill.
Automatic Correction
It is known to automatically corrects tone values of digitally stored images as disclosed in U.S. Pat. No. 5,544,258 to Levien. A histogram of the image is processed by adding a constant, applying a non-linear function to each value, and digital filtering. Application of a square root function assures that small histogram values have more contribution to the final curve, and that large histogram values have less contribution to the final tone curve, relatively. The processed histogram is integrated and then normalized for a tone correction function. This tone correction function can be applied to the average histogram of the respective individual color planes. It is applicant's view that this correction does not maintain the true colors through the correction nor deal with limitations of dynamic range.
Accordingly, there is demonstrated a need for an automatic and computationally efficient process which is capable of maintaining true color for each dot during enhancement of an image. Automation avoids the barrier of a steep learning curve to effectively adjust images for more a pleasing effect. The present invention addresses these problems by providing a technique which is both automated and which, no matter how much image-brightening is needed or what the nature of that brightening is, the color of all dots in the image are preserved.